Survey on Network Heavy Hitter Detection Methods

被引：0
|
作者：
Qian H. [1 ]
Zheng J.-Q. [1 ]
Chen G.-H. [1 ]
机构：
[1] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing
来源：
Ruan Jian Xue Bao/Journal of Software | 2024年 / 35卷 / 02期
关键词：
network heavy hitter detection; network measurement; programmable switch; sketch algorithm; software defined network (SDN);
D O I：
10.13328/j.cnki.jos.006938
中图分类号：
学科分类号：
摘要：
Network management and monitoring are crucial topics in the network field, with the technologies used to achieve this being referred to as network measurement. In particular, network heavy hitter detection is an important technique of network measurement, and it is analyzed in this study. Heavy hitters are flows that exceed an established threshold in terms of occupied network resources (bandwidth or the number of packets transmitted). Detecting heavy hitters can contribute to quick anomaly detection and more efficient network operation. However, the implementation of heavy hitter detection is impacted by high-speed links. Traditional methods and software defined network (SDN)-based methods are two categories of heavy hitter detection methods that have been developed over time. This study reviews the related frameworks and algorithms, systematically summarizes the development and current status, and finally tries to predict future research directions of network heavy hitter detection. © 2024 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：852 / 871
页数：19
相关论文
共 60 条
  • [1] Vinton GC., Formation of Network Measurement Group (NMG), IETF RFC, 323, (1972)
  • [2] Leland WE, Taqqu MS, Willinger W, Wilson DV., On the self-similar nature of Ethernet traffic, ACM SIGCOMM Computer Communication Review, 23, 4, pp. 183-193, (1993)
  • [3] Vern E., Measurements and analysis of end-to-end Internet dynamics, (1997)
  • [4] Zhou Y, Sun C, Liu HH, Miao R, Bai S, Li B, Zheng ZL, Zhu LJ, Shen Z, Xi YQ, Zhang PC, Cai D, Zhang M, Xu MW., Flow event telemetry on programmable data plane, Proc. of the 2020 Annual Conf. of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication, pp. 76-89, (2020)
  • [5] Dean J, Ghemawat S., MapReduce: Simplified data processing on large clusters, Communications of the ACM, 51, 1, pp. 107-113, (2008)
  • [6] Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I., Spark: Cluster computing with working sets, Proc. of the 2nd USENIX Conf. on Hot Topics in Cloud Computing, pp. 1-7, (2010)
  • [7] Kreutz D, Ramos FMV, Verissimo PE, Rothenberg CE, Azodolmolky S, Uhlig S., Software-defined networking: A comprehensive survey, Proc. of the IEEE, 103, 1, pp. 14-76, (2015)
  • [8] Soliman MA, Ilyas IF, Chang KCC., Top-k query processing in uncertain databases, Proc. of the 23rd IEEE Int’l Conf. on Data Engineering, pp. 896-905, (2007)
  • [9] Mirylenka K, Cormode G, Palpanas T, Srivastava D., Conditional heavy hitters: Detecting interesting correlations in data streams, The VLDB Journal, 24, 3, pp. 395-414, (2015)
  • [10] Zhang Y, Fang BX, Zhang YZ., Identifying heavy hitters in high-speed network monitoring, Science China Information Sciences, 53, 3, pp. 659-676, (2010)
123456